code: def extract_features(
      self, tokens: torch.Tensor, dense: List[torch.Tensor]
  ):
      # support passing in a single sentence
      tokens = tokens.view(-1, tokens.shape[-1])
      return self.transformer(tokens, dense)
  
imports: import re
  from typing import List, Optional
  import torch
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.nn.modules.transformer import Transformer
  from torch import nn
label_desc: Use image node_id 2 for calling the PostEncoder obj's underlying member method code with example usage: obj.extract_features(tokens, dense) and returns: self